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Postmodernism and Heterogeneity ofLeisure Tourist Behavior PatternsLuis Miguel López-Bonilla a & Jesús Manuel López-Bonilla aa Department of Business Administration and Marketing , Universityof Seville , Seville, SpainPublished online: 17 Dec 2008.

To cite this article: Luis Miguel López-Bonilla & Jesús Manuel López-Bonilla (2008) Postmodernismand Heterogeneity of Leisure Tourist Behavior Patterns, Leisure Sciences: An Interdisciplinary Journal,31:1, 68-83, DOI: 10.1080/01490400802558210

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Leisure Sciences, 31: 68–83, 2009Copyright C© Taylor & Francis Group, LLCISSN: 0149-0400 print / 1521-0588 onlineDOI: 10.1080/01490400802558210

Postmodernism and Heterogeneity of Leisure TouristBehavior Patterns

LUIS MIGUEL LOPEZ-BONILLA AND JESUS MANUELLOPEZ-BONILLA

Department of Business Administration and Marketing, University of Seville,Seville, Spain

The concept of postmodernism can be applied to the tourist sector in the search forprofiles of visitors at a tourist destination. The purpose of this study is to determinethe similarities between tourist characteristics and postmodern behavior. An analysisof latent class methodology is used to help explain the heterogeneity of tourist behavior.This diversity is demonstrated in five groups with some behavior patterns closer tomodernism and others closer to postmodernism. Further, different tendencies such asstrictly and incipient post-modernism were detected within the post-modern tourists.

Keywords behavior patterns, latent class analysis, leisure, tourist segmentation, touristbehavior

Tourist behavior has significantly changed in recent years. Travel reservations are morelikely to be made without using a travel agent’s expertise. The tendency is toward individ-ualism due to the development of new computing techniques and especially through thepossibilities of e-business. The technological age is the result of a series of socioculturalchanges taking place in advanced societies that parallel postmodernism or “post-Fordism”(Ioannides & Debbage, 1997; Lafferty & Van Fossen, 2001; Smeral, 1998). This paper isfocused on understanding the tourist behavior patterns from the characteristics of postmod-ernism in marketing.

According to Ortigueira and Encarnacion (2001), postmodern thinking originated fromFoucault’s (1968, 1984, 1994) post-structuralism, Jacques Derrida’s (1967, 1972) decon-structivism, and especially hyperrealism and Jean Baudrillard’s (1977, 1981) postmodernity.The majority of the defenders of postmodernism indicate that architecture was its first man-ifestation. Few concepts have enjoyed more popularity than postmodernism both inside andoutside academia in recent years. Yet, despite considerable discussion about postmodernismin a wide range of academic disciplines, no consensus exists about its definition (Burton,2002).

Llano (1994) established the criteria that a postmodern culture must fulfill. The post-modern culture aspires to revitalize and bring the proximity and incidence criterion up-to-date as juxtaposed to the modern culture generality criterion. The proximity and incidencecriterion is based on the idea that the product is more valuable when it is closer to and

Received 1 June 2007; accepted 28 April 2008.We express our sincere thanks to editors and anonymous journal reviewers who made useful comments and

important contributions to this paper.Address correspondence to Luis Miguel Lopez-Bonilla, Faculty of Economics Sciences and Business, De-

partment of Business Administration and Marketing, University of Seville, Ramon y Cajal Avenue, 1, 41018Seville, Spain. E-mail: luismi@us.es

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Postmodernism and Tourist Heterogeneity 69

affects a person. The generality criterion is based on the axiom that the best value is what isgood for the greatest number of subjects. It tends toward uniformity when relating realityto numbers of people. On the other hand, the emerging criterion of proximity and incidencecontributes towards heterogeneity and differentiation since it is identified with widely di-verse characteristics of people. This last criterion confers a higher value on small things ina human size. In this sense Schumacher (1983), a pioneer of postmodernism, argued thatif technology is transforming into something more inhuman, then the possibility of havingtechnology with a human face is important.

Christensen, Tord, and Firat (2005) pointed out that despite postmodernism beinga salient social theory for almost three decades, the field of marketing did not explicitlyacknowledge postmodernism as an important descriptor of the current social condition untilthe 1990s. Modern theories suggest that the product value for an individual is materializedin the benefits of the attributes of such a product. This value provides satisfaction to theconsumer. On the other hand, the focus of postmodernism refutes these statements and isbased on a series of assumptions taken from Firat and Shultz (1997) based on the frameworkdeveloped by Firat and Venkatesh (1993; see Table 1).

Postmodernism defines consumers as people who actively transform the social realityin which they prefer to live rather than adapting themselves to a way of life constructedwithout their participation. Postmodern consumers also want to experience the diversity ofmany areas and aspects of life and do not fixate on only one thing. The market becomes morefragmented because it is being built by a growing number of individual consumers (Bond &Morris, 2003). Further, the product acquired is often independent from what the consumersinitially sought in the product to satisfy themselves. Customers buy a product according tothe image it represents. In this way the consumers are not determined by the values of choicein the cost-benefit relationship but by the experiences acquired through consumption.

Several characteristics define postmodernism in culture and in marketing. Firat andVenkatesh (1993) offered five conditions of postmodern culture:

1) hyper-reality,2) fragmentation,3) reversal of consumption and production,4) decentering of the subject, and5) paradoxical juxtapositions.

Van Raaij (1993) added to these conditions the consequence of openness, which hedefined as pluralism. Brown (1993a, 1993b) also indicated three tendencies of the postmod-ern consumer: a) readiness for living in a perpetual present, b) emphasis on form/style, andc) greater acceptance of or resignation to a state of disorder and chaos. Firat and Shultz(1997) provide brief descriptions of these conditions in Table 1.

Lafferty and Van Fossen (2001) noted that the concepts of Fordism and Post-Fordismhave been discussed to some degree in the literature on tourism. Camarero (2002) adaptedthis postmodern thought to the tourist environment by suggesting that some factors that havea more direct influence on transforming tourist demand arise from the growing adhesion ofthe population to the post-materialist values in detriment to materialist values, the processof individualism, the growing value of the emotional against the rational, and the new socialdefinition of leisure. The postmodern tourism model acts as cultural reconnaissance byindicating the direction that the new tourist demand can take. However, the postmoderntourist subsists along with other types such as the family tourist as well as the traditionaland the modern tourist.

An analysis of postmodern tourist characteristics uncovers their values, desires, andtourist practices concerning behaviors related to the fragmentation and individualization of

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70 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

TABLE 1 Brief Descriptions of Postmodern Conditions in Marketing

Postmodern conditions Brief descriptions

Openness/tolerance Acceptance of difference (different styles,ways of being and living) without prejudiceor evaluations of superiority and inferiority

Hyperreality Constitution of social reality through hype orsimulation that is powerfully signified andrepresented

Perpetual present Cultural propensity to experience everything(including the past and future) in thepresent, “here and now”

Paradoxical juxtapositions Cultural propensity to juxtapose anything withanything else, including oppositional,contradictory and essentially unrelatedelements

Fragmentation Omnipresence of disjointed and disconnectedmoments and experiences in life and senseof self, and the growing acceptance of thedynamism which leads to fragmentation inmarkets

Loss of commitment Growing cultural unwillingness to commit toany single idea, project or grand design

Decentring of the subject Removal of human beings from the centralimportance they held in modern culture, andthe increasing acceptance of the potentialsof their objectification

Reversal of consumption and production Cultural acknowledgement that value iscreated not in production (as posited bymodern thought) but in consumption, andthe subsequent growth of attention andimportance given to consumption

Emphasis on form/style Growing influence of form and style (asopposed to content) in determining meaningand life

Acceptance of disorder/chaos Cultural acknowledgement that rather thanorder, crises and disequilibria are thecommon states of existence, and thesubsequent acceptance and appreciation ofthis condition

Source: Firat and Shultz (1997).

demand. “A la carte” tourism appears as juxtaposed to mass tourism. The former type oftourist shuns organized travel and tourist packages and avoids those destinations designatedas “tourist resorts.” They prefer touring different places. Younger tourists as well as thebetter-educated and those who possess a higher buying power tend toward the desire andpractice of what we have named postmodern tourism. This segment of the population alsohas greater access to the Internet. The use of the Internet for information about destinations

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Postmodernism and Tourist Heterogeneity 71

and to design itineraries and make reservations is especially adapted to the diversity andsingularity of the demands of the postmodern tourist.

Burns (2006) indicated that postmodern tourism is characterized on the supply sideby super-segmentation and quality, and on the demand side by experienced consumers,technophiles, and people who prioritize price and quality. Smeral (1998) suggested thatpostmodern visitors are in many cases first-time visitors. Further, Burton (2002) proposedthat consumers in postmodernity are fundamentally different from those in modernity whosepredictable behavior could be explained by a reliance on traditional variables such as socialclass, income, and demographics. From this postmodernist perspective, the idea of the indi-vidual stands out. Most tourism studies, however, have treated tourists as all somewhat alike(Hollingshead, 1998). Information technology facilitates individualism and also favors thepersonalization of the product from the supply side. Applying this postmodern perspective,our work is based on the distinguishing characteristics of tourists who visited Andalusia inSpain.

Latent Class Analysis

Latent class analysis is a generic name given to a class of methods for the analysis ofmulti-way contingency tables (Goodman, 1974a; Vermunt, 1997a). It attempts to explainthe associations observed between the factors that make up the table by introducing unob-servable underlying classes (Wedel & Kamakura, 2000).

The objective of the latent class analysis used in our work is to define one latent variable(i.e., no observed variable) comprised of a series of classes within which the manifestvariables (i.e., observed variables) are locally independent (i.e., within each category ofthe latent variable, the manifest variables are statistically independent, which is sometimescalled criterion conditional independence; Van der Ark & Richards, 2006). If such a variablecan be defined, then its classes are taken to represent either the latent types or the categoricalscale locations of the variable as they are defined by the variables measured within thesampled population (McCutheon, 1987).

For example, consider a latent class model for latent variable X developed from twomanifest variables A (with I categories) and B (with J categories). Let

πXt = probability of being in latent class t (t = 1, 2, 3, . . . , T) of latent variable X

πA/Xit = conditional probability that an observation has response i (i = 1, 2, . . . , I) to

the manifest variable A given the observation is in latent class tπB/X

jt = conditional probability that an observation has response j (j = 1, 2, . . . , J) tothe manifest variable B given the observation is in latent class t

πABXijt = probability that an observation in latent class t has response i and j

Using the above probabilities, local independence can be expressed as:

π ABXijt = π A/X

it × π B/Xjt × π X

t (1)

The objective is to develop a model with the fewest number of latent classes Tthat explain the association among the manifest variables. The probability of an indi-vidual being at level i, j with respect to the joint variable A, B can be expressed asfollows:

πijt =T∑

t=1

π AB Xijt =

T∑t=1

(π

A/Xit × π

B/Xjt × π X

t

)(2)

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72 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

Each of the latent classes is then characterized and named based on the estimatedconditional probabilities. The extension of this latent class model to more than two manifestvariables consists of additional probabilities for the manifest variables.

The modeling procedure begins with T = 1 latent class and continues adding latentclasses until a suitable model is detected that has an adequate fit based on several goodnessof fit criteria (e.g., Akaike Information Criterion; AIC; Akaike, 1987, Bayesian InformationCriterion (BIC)). The steps are going to be done in this analysis are: a) fit the best T classesmodel, b) classify the observations into the new latent classes, and c) delete any possibleunnecessary manifest variables.

The analysis of the latent classes can be carried out with a variety of programs. In thispaper we employed the LEM program (i.e., log-linear using the EM algorithm) developedby Vermunt (1997a; 1997b). This model was applied to tourist behavior in a community inSpain.

Application of the Latent Class Model to Tourist Behavior in Andalusia

Andalusia is an Autonomous Community of Spain. This region is located in the southwest ofEurope and is made up of eight provinces. Three provinces (Almerıa, Granada, & Malaga)are on the Mediterranean coast, two provinces (Huelva & Cadiz) are on the Atlantic coast,and three provinces (Cordoba, Jaen, & Sevilla) are inland. The Andalusian tourism sectorprovides 11% of the region’s Gross National Product (GNP). Andalusia is the main touristdestination of Spanish residents and the fourth most popular Spanish destination for inter-national tourists. Eight million national tourists and 28 million international tourists visitedAndalusia in 2005 (Analistas Economicos de Andalucıa, 2006).

The data analyzed in this study were obtained from the Andalusia Survey on Tourism2005 database. The survey characteristics are described on the Web page of the IEA (Institutode Estadıstica de Andalucıa, 2006).

The sample selection method was quota sampling. The sample was obtained fromstratification into 24 geographical zones, which reflected the territorial distribution of thetourists during the four quarters of the year. We used a continuous survey of quarterlyperiodicity in which interviewers investigated for several days to complete the number ofinterviews assigned to the place. The population included the set of people who travelledaround Andalusia as tourists. Tourism was defined according to the World Tourism Orga-nization (WTO, 1993). Thus, tourism comprised the activities of persons traveling to andstaying in places outside their usual environment for not more than one consecutive yearfor leisure, business, and other purposes. Permanent residents of Andalusia were eliminatedfrom the sample.

The questionnaire was answered by 8,542 people. However, the sample we used in-cluded 4,658 people since various filters were used to adapt it to the conditions of ourresearch. We selected only those answers given by people who traveled to Andalusia forleisure and holiday reasons. Moreover, we eliminated questionnaires from people whoseusual residence was Andalusia as well as questionnaires from those who spent every holidayin this region. The assiduous repetition of the holiday could interfere with the identifica-tion of the behavior patterns of postmodern tourists. The majority of the respondents werefrom Spain (34%) followed by the United Kingdom (16%), Germany (14%), France (7%),Portugal (4%), the United States (3%), and Italy (3%). No other country represented morethan 2% of the respondents.

According to the postmodernism conditions in marketing, the manifest variables thatwere employed in this study were organization, expenditures, satisfaction, information, andprevious experience. Each is described briefly.

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Postmodernism and Tourist Heterogeneity 73

Organization (O)

Postmodern tourists are characterized as having greater control over the order they wishto see in their lives. The organization of the trip indicates who organized the trip and isstructured into two categories: a privately organized trip by contracting the tourist serviceproviders directly (O1), and a trip organized through an intermediate party such as a travelagent, a club, an association, a company, or another entity (O2). The former category isclose to a postmodern tourist behavior, while the latter distances itself from this behaviorpattern.

Expenditure (E)

Postmodern tourists are inclined to experience everything in the present and to juxtaposeanything with anything else as shown in Table 1. The expenditure of tourists in destinationsis linked to these postmodernism conditions. The percentage of the expenditure in the placeof origin was the proportion of the total cost of the trip paid at the tourist’s place of origin.Expenditures are divided into four categories when the expenditure at the place of originrepresents more than 75% of the total cost of the trip (E1), between 50% and 75% of thetotal cost of the trip (E2), between 25% and 49.9% of the total cost of the trip (E3), andless than 25% of the total cost of the trip (E4). When tourists spend less at their place ofresidence, a postmodern behavior profile is approached.

Satisfaction (S)

Postmodern consumers recognize that they are not just a consumer, but a customizer and aproducer at each consumptive moment (Firat, Dholakia, & Venkatesh, 1995). In this way,a customizer should obtain a greater satisfaction than a customer. Satisfaction is consid-ered from the point of view of the Expectancy Disconfirmation theory (e.g., Oliver, 1997)where satisfaction is a comparative response between the expectations and the performanceoutcomes.

Satisfaction conveys the quality perceived by the tourist in the course of the visit.This variable is structured into five categories: very high (S1), high (S2), normal (S3), low(S4), and very low (S5). One of the identifying characteristics of postmodern tourists ishaving a clear perception of what they expect to receive on their trip. Thus, the distortionor gap between the expectations of the tourist and the perception of the services received isexpected to be less than for another type of tourist. Hence, we suggest that high satisfactioncan be a trait of the postmodern tourist, while a lower satisfaction could identify other typesof tourists.

Information (I)

Information technologies are a basic resource of postmodern consumers. Sources of infor-mation provide a way to become aware of the tourist destination. It distinguishes the meansof information that has been used to obtain knowledge of the destination. This variable isstructured into seven categories: travel agent’s recommendation (I1), recommendation fromfamily or friends (I2), own experience (I3), tourist brochure (I4), advertisement (I5), theInternet (I6), and other (I7). The profile of the postmodern tourist describes the person whouses new technology as a source of information. On the other hand, the use of traditionaltravel agents corresponds to a different tourist profile.

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74 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

Previous Experience (P)

Previous experience refers to whether the tourist has previously visited Andalusia. Thevariable is binary: yes (P1) or no (P2). The answers from Andalusian residents and frompeople who regularly spend their holidays in this destination were eliminated by filteringthese elements from the sample. However, tourists who have already visited the region butwho cannot be confirmed as regular visitors were included in the sample. The placing intoone of these categories was independent of the behavior of a postmodern tourist. In otherwords, giving a positive answer was not sufficient to identify this type of visitor. Neverthe-less, this variable clarified certain elements that could have distorted this identification andthat could mistakenly qualify the behavior of a standard tourist as postmodern. Hence, weunderstand that previous experience of the tourist destination is like a veil that impedes therecognition of those types of behaviors that may be special in postmodern tourists. In thisway, the importance of the control of this variable’s influence is derived.

From cross-referencing the different categories of each of the five variables described,contingency Table 2 was obtained. This supports the application of the proposed statisticaltechnique in this study. The number of observations in Table 2 is not evenly distributed.This contingency table indicated that the population was not homogeneous with respect tomanifest variables and could be divided into several segments (e.g., Goodman, 1974a). Thedifference in the number of elements in the cells of Table 2 is necessary for the applicationof this methodology because it indicates the heterogeneity of the latent structure (see, e.g.,Goodman, 1974b). However, as the number of manifest variables increases the frequencytable of the response patterns becomes sparse, which invalidates the p-values obtained fromthe χ2 and the log-likelihood ratio tests. Therefore, an alternative was to use model selectioncriteria such as the AIC or the BIC as robust statistical methods (Moustaki & Papageorgiou,2005).

Results of the Research

We first sought the existence of independence between the manifest variables. We wantedto answer the question of whether the existence of a latent variable with distinct categoriesthat explains the association between the observed variables could be confirmed (Bhatnagar& Ghose, 2004; Hagenaars & McCutheon, 2002; Sanchez Rivero, 2000). A goodness-of-fitindicator derived from the information theory was used (i.e., BIC); Schwarz, 1978). Thisindicator considers the distorting effect that can be provoked by a large sample size. Linand Dayton (1997) indicated its suitability when dealing with several thousands of cases.

The formulas of this indicator are presented in two versions shown in Table 3. Thesetwo versions are equally useful for the comparison and selection of the model that fits thedata better. The preferred model is the one that provides the lowest value of the BIC ineither of the versions. (e.g., Kass & Raftery, 1995; Rudas, Clogg, & Lindsay, 1994; Wedel& Kamakura, 2000).

Nine models were compared sequentially in such a way that each model representeda different number of latent classes. The first model supposes that all the elements of thesample are included in the same latent class such that there is no heterogeneity of the dataaccording to the observed variables. In the second model the individuals were divided intotwo different segments. The third included three segments and so on successively untilarriving at a model with nine latent classes (i.e., models with a positive number of degreesof freedom).

The indicators showed some improvement in the model fit as the number of latentclasses increased up to five (see Table 4). This information confirmed the existence of

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Postmodernism and Tourist Heterogeneity 75

TABLE 2 Cross-referenced Classification of the Manifest Variablesab

S1 S2 S3 S4 S5

O1 O2 O1 O2 O1 O2 O1 O2 O1 O2

P1 P2 P1 P2 P1 P2 P1 P2 P1 P2 P1 P2 P1 P2 P1 P2 P1 P2 P1 P2

I1 E1 1 11 2 78 0 16 2 61 0 2 2 11 0 1 1 0 0 0 0 0E2 2 9 5 62 3 15 8 119 1 4 2 14 0 0 0 1 0 0 0 0E3 2 11 2 28 2 14 3 42 1 2 0 5 0 0 0 0 0 0 0 0E4 1 8 2 6 1 9 1 4 0 3 0 3 0 0 0 0 0 0 0 0

I2 E1 3 41 7 66 1 52 6 81 0 9 3 11 0 1 0 3 0 0 0 0E2 20 62 9 57 21 154 14 139 11 23 4 23 2 4 2 1 0 0 0 0E3 29 116 4 12 44 238 19 39 9 34 2 2 2 1 0 1 0 0 0 0E4 52 142 3 6 109 241 4 12 18 44 0 1 7 10 0 1 0 2 0 0

I3 E1 5 0 21 2 3 6 7 12 2 4 0 2 1 0 0 0 0 0 0 0E2 24 3 21 0 27 38 32 6 11 16 0 2 2 0 1 0 0 0 0 0E3 48 4 6 0 71 66 12 3 15 20 2 1 1 0 0 0 0 0 0 0E4 159 7 3 0 237 44 8 0 46 7 0 0 8 4 0 0 0 0 0 0

I4 E1 2 5 1 10 1 9 2 6 0 0 0 2 0 0 0 0 0 0 0 0E2 2 8 2 9 2 13 3 8 0 3 0 2 0 0 0 0 0 1 0 0E3 4 12 0 2 2 15 3 5 0 5 0 3 0 0 0 0 0 0 0 0E4 10 29 1 2 4 20 0 0 0 2 0 0 0 2 0 0 0 1 0 0

I5 E1 0 2 0 2 0 10 0 12 0 2 0 1 0 0 0 0 0 0 0 0E2 0 3 0 0 0 7 1 6 0 1 0 1 0 0 0 1 0 0 0 0E3 1 8 0 2 3 6 1 2 0 1 0 3 0 0 0 0 0 0 0 0E4 10 11 0 1 5 13 0 0 1 5 0 0 0 0 0 0 0 0 0 1

I6 E1 0 13 0 15 0 11 4 19 0 0 0 3 0 0 0 1 0 0 0 0E2 3 16 3 23 5 28 0 15 4 8 0 4 0 0 0 1 0 0 0 0E3 4 15 0 3 8 40 2 9 0 9 0 3 0 3 0 2 0 0 0 0E4 15 75 0 3 15 47 3 2 1 6 0 0 0 1 0 0 0 0 0 0

I7 E1 2 10 1 7 4 6 2 2 1 0 0 0 0 0 0 0 0 0 0 0E2 12 21 3 5 9 9 2 4 0 6 0 1 0 0 0 0 0 0 0 0E3 8 12 0 1 1 7 2 8 0 0 1 2 0 0 0 0 0 0 0 0E4 14 13 1 9 19 23 3 6 0 2 1 2 0 1 0 0 0 0 0 0

a = The manifest variables are O = Organization, E = Expenditure, S = Satisfaction, I = Infor-mation and P = Previous Experience

b = The numbers inside the cells are the number of people in the sample

heterogeneity in the set of Andalusia visitors used in the investigation and suggested theexistence of latent segments (Kemperman & Timmermans, 2006).

The model, which presents indicators of the best fit (BIC (L2) = −3437.26 and BIC(log) = 44688.04), is one of five latent classes, as shown in Table 4. Hence, this numberof segments best explains the heterogeneity of the information in relation to the observedvariables used.

Once the model of five classes was selected as the best representation of the realcollected information, it is convenient to show the maximum-likelihood estimations of theprobabilities of belonging to each of the latent classes and the conditional probabilitiescould be shown in Table 5.

The latent class probabilities are the proportions of population that are associatedwith each of the five classes. The conditional probabilities indicate the probability that an

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76 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

TABLE 3 Formulas for the Calculation of the Bayesian Information Criterion (BIC)

Log version L2version

loge (Likelihood) = ∑observed frequency

+ loge

(T∑

t=1πA/X

it × πB/Xjt ×... × πE/X

mt × π Xt

) L2 = 2∑

observed frequency ×loge (observed frequency/expectedfrequency)

BIC = − 2 loge (Likelihood) + loge (sample size)× number independent parameters

BIC = L2 − loge (sample size) × df

observation in a latent class will score a particular way on an observed measure. From theseprobabilities, the characteristics of the five classes of respondents (i.e., described as A, B,C, D, and E) were identified in the analysis.

CLASS A represented 26% of the population. Individuals organize their travel ar-rangements themselves and they spend less than 25% on the organization of the trip fromtheir usual place of residence and more than 75% in the destination. Their satisfactionwith their stay in Andalusia was very high. This group expressed the highest perceivedquality and were delighted (i.e., had a high zone of delight) with the experience. In thewords of Santos and Boote (2003), “Delight can be considered to be the affective state thatmay exist when a consumer’s desired expectations are positively disconfirmed” (p. 147).Oliver (1997, 2000) considered delight as an affective state that appears when observedperformance is better than expected. The means of information most used by CLASS Arespondents was the recommendation from family or friends. This group also had the high-est rate for searching the Internet for information on the tourist destination (i.e., morethan double than any of the other classes). Further, this group most responded to ra-dio, press, and television advertisements and advertising brochures. In addition, hardlyany of the individuals included in this class had visited Andalusia previously. CLASS Ahad characteristics nearest to the postmodern tourist profile and was denoted as strictlypostmodern.

CLASS B was made up of almost 8% of the population. These individuals tendednot to make their own travel arrangements although about 38% of the group did organizethe trip themselves. Between 25% and 50% of the total budget for the trip was spentat the destination with most of this expense (i.e., 50–75%) is accounted for in travelingfrom where the tourist normally resides. The satisfaction was high but did not reach itsmaximum value. The principal means of information was personal experience since 100%

TABLE 4 Models of Latent Classes Associated to the Data Observed

loge Number ofModel likelihood L2 parameters df BIC(L2) BIC(Lg)

Independence −23959.9519 4516.1079 15 544 −78.7018 48046.5991Two classes −22710.1433 2016.4906 31 528 −2443.1776 45682.1232Three classes −22231.3490 1058.9022 47 512 −3265.6247 44859.6762Four classes −3366.8758 822.5096 63 496 −3366.8758 44758.4251Five classes −22010.3912 616.9864 79 480 −3437.2575 44688.0434Six classes −21967.2076 530.6192 95 464 −3388.4832 44736.8177Seven classes −21937.9530 472.1101 111 448 −3311.8509 44813.4500Eight classes −21925.2882 446.7806 127 432 −3202.0389 44923.2619Nine classes −21903.9940 404.1920 143 416 −3109.4860 45015.8148

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Postmodernism and Tourist Heterogeneity 77

TABLE 5 Probabilities of Latent Classes and Conditional Probabilities of Five-segmentsModel

CLASS CLASS CLASS CLASS CLASSA B C D E

Organization ofthe trip (O)

Privately (O1) 0.9362 0.3794 1.0000 1.0000 0.1141Intermediate (O2) 0.0638 0.6206 0.0000 0.0000 0.8859

% expenditure inthe place oforigin (E)

> 75 (E1) 0.1111 0.1920 0.0000 0.0353 0.3738Between 50 and 75 (E2) 0.1745 0.4562 0.0635 0.2485 0.4516Between 25 and 50 (E3) 0.2388 0.2329 0.1925 0.4218 0.1543< 25 (E4) 0.4755 0.1189 0.7440 0.2944 0.0204

Satisfaction withthe stay (S)

Very high (S1) 0.5199 0.4146 0.3666 0.0619 0.3465High (S2) 0.4048 0.4970 0.5160 0.7500 0.5517Normal (S3) 0.0570 0.0710 0.0971 0.1741 0.0920Low (S4) 0.0142 0.0174 0.0202 0.0140 0.0097Very low (S5) 0.0041 0.0000 0.0000 0.0000 0.0000

Sources ofinformation (I)

Travel agencies (I1) 0.0321 0.0733 0.0000 0.0172 0.4015Family or friends (I2) 0.5345 0.2638 0.2370 0.6290 0.3940Own Experience (I3) 0.0000 0.4714 0.6525 0.2476 0.0257Tourist brochure(I4) 0.0933 0.0461 0.0138 0.0202 0.0408Advertisement (I5) 0.0491 0.0023 0.0188 0.0111 0.0279Internet (I6) 0.1813 0.0366 0.0362 0.0748 0.0843Other (I7) 0.1097 0.1065 0.0417 0.0000 0.0259

Previousexperience (E)

Yes (E1) 0.0547 1.0000 0.9869 0.0863 0.0279

No (E2) 0.9453 0.0000 0.0131 0.9137 0.9721Latent Class Probabilities 0.2630 0.0782 0.1937 0.1971 0.2680

of the tourists in CLASS B had visited Andalusia before and they represented the classwhich, along with CLASS C, least consulted the Internet.

This CLASS B tourist was defined as not very independent given that despite knowingthe destination, they had some behavior parameters that led them to the destination as if itwere the first time that they visited. CLASS B represented the opposite pole to the profileof the postmodern tourist and was therefore, denoted as the Antipodes of the postmoderntourist.

CLASS C represented slightly over 19% of the population. These individuals organizedtheir own travel arrangements and spent more than 75% of the total cost of their trip at thedestination. They spent the most at the destination and the least at the point of origin (i.e.,less than 25%). The means of information fundamentally used was personal experience,and together with CLASS B used the Internet least.

This CLASS C group can be characterized as Experience of the Destination. Theprofile of this group was similar to that qualified as Strictly Postmodern in relation to theorganization of the trip and the distribution of the expenditure. However, tourists withprevious experience with the destination could distort the observation of the identifyingparameters of postmodernism, since a tourist with experience has some traits that are similarto those of the postmodern tourist for whom the destination is unknown. Hence, in CLASSC a great number of postmodern tourists were included with 37% delighted and satisfiedwith the trip. However postmodern tourists were impossible to distinguish from the rest ofthe class due to the influence of the experience variable.

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78 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

CLASS D made up almost 20% of the respondents. These individuals organized theirown trips. The majority spent between 25% and 50% of the total expenditure in the placeof residence. About 75% said the satisfaction with their stay was high, although CLASS Dhad the least number of individuals reflecting high delight. The most popular means ofinformation was via friends or family, and the use of Internet was about average comparedto all the classes. More than 91% of the individuals had no previous experience with thedestination.

This CLASS D can be qualified as the transition between classic or modern behaviorand postmodern behavior. The fundamental difference with the latter is in the distribution ofthe budget, which, although it was greater at the destination, did not exceed three-quartersof the total cost for the trip. This class was denoted as Incipient postmodern or conservativepostmodern.

CLASS E represented about 27% of the population. This group had the highest quantityof individuals who did not organize the trip themselves (89%). The majority spent between50% and 75% of the total expenditure in the place of origin. This group had the highestnumber of people who spent more than 75% of the total cost of the trip in the place ofresidence (37%). Few of these tourists spent more than 75% at the destination (2%). Theperception of the quality of the stay was high and they were satisfied. Travel agents providedthe principal source of information (40%), which the other groups seldom used. The secondmost used means of information was recommendations from family and friends. Finally,CLASS E had the highest proportion of tourists who had never visited Andalusia previously.

This segment was classified as Classic or modern tourists since without previous knowl-edge of the destination they sought package deals from travel agencies and other interme-diaries. They paid the greatest part of the cost of the trip at the point of origin and not at thedestination.

The next step in analysis was to assign each individual represented by an answer pattern(i.e., the response given to the manifest variables) to one of the five distinct latent classes.Bayes’ theorem was used to determine the probabilities of belonging to each of these classes.In this way, the probability of an individual of any particular answer pattern, representedby the cell ijklm, belonging to any class t is defined by the expression P[X = t /ABCDE]and is calculated as (e.g., Goodman, 1974a):

π X/AB...Eij...mt = π AB...EX

ij...mt /∑

t

π AB...EXij...mt (3)

Once the probabilities for each of the latent classes were calculated, the rule of assig-nation is based on the modal probability (i.e., the class assigned is the one of highestprobability). In the following figure this assignation is given.

In assigning each element to a single class by using the modal probability, a certainerror is committed, which can be estimated by means of the coefficient of the correctlyallocated percent (λ) and the rate of error (E). Lambda (λ) and the rate of error (E) arenot complementary means (i.e., λ + E is not one; Clogg, 1995; McCutheon, 1987; Dayton,1999). In this case, their values are 0.7572 and 0.1777, respectively, and represent acceptablemagnitudes. The λ is better when it is near 1 and E is better when it is near to zero.

From Figure 1, the combinations of the values of the manifest variables can be observed,which determine that an individual belongs to the assigned latent class. Thus, by knowing thetourists’ answers to the different variables, which latent class they are going to be assignedcan be determined with a high certainty. Once the answer patterns that characterize everyone of the five latent classes are known, classifying an individual into a segment as a functionof the values of the manifest variable is possible.

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Postmodernism and Tourist Heterogeneity 79

FIGURE 1 Assignation of the distinct answer patterns to each of the latent classes of themodela.a = The letters into the cells show the latent class assigned to each element of the sample(A = Strictly postmodern, B = Antipodes, C = Experience, D = Incipient postmodern,and E = Classic)

People who had a very low satisfaction with the stay (S5) belonged to CLASS A, butit is necessary to indicate that this value of manifest variable S was rare since only 0.11%of the sample indicated this answer (e.g., in Table 2 only five persons of the sample gavethis answer).

The individuals who responded with both O1 (i.e., they organized the trip themselves)and P2 (i.e., no previous knowledge of the tourist destination) belonged to CLASS A or D.If also the sources of information that were used by the tourist was the own experience (I3)and they had a very high satisfaction with the stay (S1), or the means the information usedwas the recommendation from family and friends (I2) and the satisfaction with the stay wasnot very high or very low, the people belong to CLASS D. In other cases, they belonged toCLASS A.

The people who answered both O2 (i.e., they did not organize the trip themselves) andP1 (i.e., they had visited the destination previously) had a high probability of belonging toCLASS B. IF also the expenditure in the place of origin represented more than 25% of thetotal cost of the trip (E1,E2 and E3), the main source of information was the advertisement(I5), the satisfaction with the stay was not very low (S1, S2 or S3), then tourists belongedto class E.

The individuals who respond both O1 (i.e., organized their own trip) and P1 (i.e., theyhad visited the destination previously) belonged to CLASS B or C. If they also answeredI1 (i.e., travel agent’s recommendation) or E1 (i.e., expenditure in the place of origin morethan 75% of the total cost of the trip) and I2 (i.e., recommendation from family and friends),

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80 L. M. Lopez-Bonilla and J. M. Lopez-Bonilla

I4 (i.e., tourist brochure) or I7 (i.e., other sources of information), they belonged to CLASSB. In other cases, they belonged to CLASS C.

The people who answered both O2 (i.e., they did not organize the trip themselves) andP2 (i.e., no previous experience of the destination) probably belonged to class E. If alsothey respond that they had a satisfying stay between median to very high (S1, S2, S3), theexpenditure in the place of origin represented less than 25% of the total cost of the trip(E4), and the sources of information used were not a travel agent’s recommendation or ownexperience (I2, I4, I5, I6, I7), then they belonged to CLASS A.

The set of manifest variables used to classify potential tourists according to their re-sponses possessed a core of special indicators. Therefore, reducing the number of questionswith hardly any loss of information was possible. The questions about who organized thetrip and whether this destination had already been visited made it possible to distinguishbetween four types of segments: Antipodes of postmodern (B), Experienced (C), Classic(E) and Postmodern (A∪D). However, to differentiate between segments denominated asStrictly Postmodern (CLASS A) and Incipient postmodern (CLASS D), two further itemswere used: the sources of information used and the perceived quality. Hence, with the an-swers from the manifest variables O and P, we identified the postmodern tourists, whetherthey be Strictly or Incipient postmodern, from the rest of the groups. Knowing the answerto items I and S is necessary to be able to differentiate the Incipient postmodern from theStrictly postmodern tourist.

Conclusions

We investigated the heterogeneity of tourist behavior in Andalusia in this study. We aimedto discover those groups whose characteristics were close to postmodern behavior, thoseothers who were far from this profile, and those aspects which differentiate the behaviors.

From the proximity of the ideal or prototypical characteristics of a postmodern con-sumer, analysis of latent classes was used to describe some segments of tourists who visitedAndalusia in the year 2005. The results identified the optimal classification as five groupsdenoted as Classic, Experienced, Strictly postmodern, Antipodes of postmodern, and In-cipient postmodern. This classification was the starting point for effective segmentationmarketing. Firat and Shultz (1997) suggested that marketers may need to develop differ-ent conceptions and approaches to segmentation and positioning if they wish to achievemarketing objectives in postmodernism.

Each of these groups was characterised with respect to five manifest variables that pos-sessed diverse characteristics. Knowing the segment to which a tourist belongs is importantfor the management of both public and private organizations related to the tourist destinationand for translating the segmentation into integrated strategic and tactical actions.

A utility of this classification is to be able to classify a new individual (i.e., someonewho does not belong to the sample used here) into one of the five segments. We identifiedthe answers that should be obtained from a person to be classified into one of these groups.These indicators may permit the immediate a priori classification of a tourist. Being able todetermine rapidly what type of behavior pattern is to be expected for a specified potentialconsumer may be important.

Our results indicated that postmodern tourists can be distinguished from the Classicor modern tourists because the former organize their own travel arrangements while thelatter tend to refer to a travel agency. The postmodern tourists are differentiated from theExperienced tourists because the latter already know the destination from previous visits,while the postmodern tourists do not. The postmodern tourists are different from the oneswho we have denoted as the Antipode of postmodern because Antipodes already know the

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Postmodernism and Tourist Heterogeneity 81

destination in the same way as the experienced tourists, and do not organize their owntrip. Finally, we have determined two groups of postmodern tourists: Strictly and Incipientpostmodern, whose fundamental difference lies in the sources of information employedand the most common use of new technology and advertising, which are more used by theStrictly postmodern tourists. There is also a second difference between postmodern tourists,to a lesser degree, in the value of the satisfaction perceived, which tends to be higher withinthe Strictly postmodern segment too.

From a commercial point of view the tourism industry at a tourist destination will preferto be used by Strictly or Incipient postmodern tourists more than Classic or Antipodes ofpostmodern. On the other hand, the tourism industry that is situated in the site of origin ofthe potential tourist will prefer the segments Classic and Antipodes to Strictly and Incipient.The marketing actions of these industries should be able to adapt to the segments of tourismthat actually produce the sustainable competitive advantage.

An important limitation of this work is that we have used some measurement variablesthat we think are related to the concept of postmodern tourism. It is likely that other manifestvariables, which we have not included in this investigation, are influenced by this topic.However, this work is a first approximation to identify some segments of tourists that maybe important for the development of the tourism industry in Andalusia as well as in otherplaces around the world.

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